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Data Clustering on Breast Cancer Data Using Firefly Algorithm with Golden Ratio MethodDEMIR, M. , KARCI, A.
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artificial intelligence, heuristic algorithms, clustering algorithms
algorithm(30), optimization(24), applications(12), karci(11), search(7), sciences(7), intelligence(7), inspired(6), global(6), evolutionary(6)
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About this article
Date of Publication: 2015-05-31
Volume 15, Issue 2, Year 2015, On page(s): 75 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02010
Web of Science Accession Number: 000356808900010
SCOPUS ID: 84979827793
Heuristic methods are problem solving methods. In general, they obtain near-optimal solutions, and they do not take the care of provability of this case. The heuristic methods do not guarantee to obtain the optimal results; however, they guarantee to obtain near-optimal solutions in considerable time. In this paper, an application was performed by using firefly algorithm - one of the heuristic methods. The golden ratio was applied to different steps of firefly algorithm and different parameters of firefly algorithm to develop a new algorithm - called Firefly Algorithm with Golden Ratio (FAGR). It was shown that the golden ratio made firefly algorithm be superior to the firefly algorithm without golden ratio. At this aim, the developed algorithm was applied to WBCD database (breast cancer database) to cluster data obtained from breast cancer patients. The highest obtained success rate among all executions is 96% and the highest obtained average success rate in all executions is 94.5%.
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 A modified firefly algorithm for global minimum optimization, Yelghi, Aref, Köse, Cemal, Applied Soft Computing, ISSN 1568-4946, Issue , 2018.
Digital Object Identifier: 10.1016/j.asoc.2017.10.032 [CrossRef]
 A novel hybrid knowledge of firefly and pso swarm intelligence algorithms for efficient data clustering, Danesh, Malihe, Shirgahi, Hossein, Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Issue 6, Volume 33, 2017.
Digital Object Identifier: 10.3233/JIFS-17170 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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